Discriminatory Analysis of Discharged Gas and Heavy Oils in the Sea
Bibliographic record
Abstract
Gas and heavy oils discharged into the sea were discriminated using gas chromatography-mass spectrometry (GC-MS), gas chromatography (GC-FID) and infrared spectroscopy (FT-IR). The GC-MS focused on the determination of the biomarkers, such as hopanes, norhopanes and triaromatic steranes, which were detected from heavy oil, but were hardly observed from gas oil. The discriminative analysis using GC-FID of the methylnaphthalenes showed a discrimination with a ratio of ((2-methylnaphthalene)+(1-methylnaphthalene))/tridecane. The ratios for the gas oils were less than 1.0, but those for the heavy oils were 1.0 or higher. These oils were distinguished in comparison to the FT-IR data from three peaks at 811, 742 and 723 cm -1 which were assigned to the CH bending modes for 2 and 4 hydrogens and a methylene framework, respectively. The order for the heavy oil was 723 < 742 < 811 cm -1 , while that for the gas oil was 742 < 811< 723 cm -1 . Moreover, the absorption intensity at 1603 cm -1 for the heavy oil was higher than that for the gas oil. An absorption at 475 cm -1 (out-of-plane ring vibration) was also observed for the heavy oil, but not for the gas oil. In combination of the GC results with the FT-IR, 2-methylnaphthalene and 1-methylnaphthalene were contained in greater amounts in the heavy oil than in the gas oil, which were derived from the light cycle oil. Thus, the heavy oils discharged from ships and drifted on the seashore were discriminated from the original heavy oils and the gas oils. Key words : Heavy oil, Gas oil, Identification, Methylnaphthalene, Hopane
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".